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In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

We need human behavioural change to decarbonize our buildings. This requirement arises from our needs, lifestyle energy choices and interactions with buildings, and is an underexploited, yet essential demand-side opportunity for rapid and sustainable decarbonization. We propose a sufficiency-oriented approach that fosters equitable building decarbonization, while maintaining planetary boundaries.

The increasing frequency and severity of weather extremes caused by climate change evidence the need to assess buildings beyond their typical thermal and energy performance under normal operation. It is also essential to evaluate thermal resilience to safeguard occupants' health during extreme events and power outages. This study proposes a simulation framework to evaluate and enhance the thermal resilience of buildings against indoor overheating using an integrated set of performance metrics. This work also addresses how to aggregate resilience profiles of single buildings into the urban scale, supporting the evaluation of thermally resilient communities. This is the first step to connecting building and urban scales in a resilience analysis, seeking to further address other stakeholders' needs in the future. The application of the framework is exemplified through a case study considering three different climates in Brazil. This analysis allowed identifying cases with poor thermal resilience and essential dependence on air conditioning to guarantee the survivability of occupants during extreme hot weather. Nonetheless, by only changing the envelope's thermal transmittance and thermal mass, buildings' thermal autonomy increased up to 65% points and cooling loads were reduced by up to 61% in the hottest climate, São Luís. However, additional strategies are necessary to mitigate remaining indoor extreme thermal conditions, such as solar shading and increased air movement.

With climate change leading to more frequent, more intense, and longer durations of extreme weather events such as heat waves and cold snaps, it is essential to maintain safe indoor environmental conditions for occupants during such events, which may coincide with, or even cause, power outages that expose residents to health risks. Analyzing the impacts of extreme weather events on the thermal resilience of buildings can help stakeholders (including occupants) understand the risk and inform them about mitigation and adaptation actions. Moreover, analyzing the technological, social and policy dimensions of thermal resilience is critical for climate-proofing buildings. This paper presents 10 questions that highlight the most important issues regarding the thermal resilience of buildings for occupants in the face of climate change. The proposed questions and answers aim to provide insights into current and future building thermal resilience research and applications, and more importantly to inspire new significant questions in the field.

Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

Rapid urbanization pressure and poverty have created a push for affordable housing within the global south. The design of affordable housing can have consequences on the thermal (dis)comfort and behaviour of the occupants, hence requiring an occupant-centric approach to ensure sustainability. This paper investigates occupant behaviour within the urban poor households of Mumbai, India and its impact on their thermal comfort and energy use. This study is a first-of-its-kind attempt to explore the socio-demographic characteristics and energy-related behaviour of low-income occupants within Indian context. Three occupant archetypes, Indifferent Consumers; Considerate Savers; and Conscious Conventionals, were identified from the behavioural and psychographic characteristics gathered through a transverse field survey. A two-step clustering approach was adopted for occupant segmentation that highlighted considerable diversity in occupants’ adaptation measures, energy knowledge, energy habits, and their pro-environmental behaviour within similar socio-economic group. Building energy simulation of the representative archetype behaviour estimated up to 37% variations for air-conditioned and up to 8% variation for fan-assisted naturally ventilated housing units during peak summer months. The results from this study establish the significance of occupant factors in shaping energy demand and thermal comfort within low-income housing and pave way for developing occupant-centric building design strategies to serve this marginalized population. The developed low-income occupant archetypes would be useful for architects and energy modelers to generate realistic energy use profiles and improve building performance simulation results.

Agent-based modeling is an advanced computational technique capable of representing complex and dynamic processes of human behavior in building performance simulation. Though the agent-based approach supports diverse applications concerning human behavior modeling within the built environment, there is no consensus on the optimal amount of information or level of granularity needed for occupant information representation. This paper attempts to formalize the level of details (LoD) needed for occupant behavior representation in agent-based environments. A novel framework, grounded on the concept of LoD, is proposed to select the required details in representing occupants in agent-based models. Ten attributes related to occupants' presence, movement, behavioral processes, and repertoire are considered to define the LoD. The framework identifies use case parameters as the guiding principle and allows a hybrid approach for selecting varying degrees of occupant attributes to serve the purpose of simulation. A discussion on the pertinence of different occupant behavior LoDs in relation to the desired objective and simulation context is also presented. The study intends to support the occupant behavior research by advancing agent-based occupant modeling in building performance simulation.

The complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants' schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

Thermal adaptation in low-income housing of Mumbai, India is investigated using a longitudinal field study and in-situ field measurements. 705 set of responses from two different neighbourhoods were analysed to understand the patterns of behavioural adaptation. Spatial configuration, temporal factors and underlying societal norms influenced the comfort-related behaviour in low-income housing. Clothing adaptation was primarily governed by the gendered socio-cultural practice of purdah system and western influences rather than thermal needs. Logistic regression predicts probabilities of 88% and 20% for using ceiling fans and exhaust fans respectively at 80% indoor air relative humidity. However, thermal stimuli cannot predict occupant behaviour well due to presence of non-thermal determinants. Security, privacy, environmental nuisances (dust, noise and odour) and insects or animals menace (mosquitoes, monkeys, rats, lizards) were the major barriers to thermal adaptation. The study is helpful in evaluating the influence of occupant behaviour on building performance and thus to inform building design and operation. The policy implications could be through the development of design guidelines for the housing schemes to create thermally comfortable low-income dwellings.

Indoor thermal comfort is critical to building sustainability besides improving occupants' health, well-being and productivity. However, the applicability of the existing comfort standards within different climatic conditions and contextual settings is often in question. This study presents the findings from a longitudinal thermal comfort study conducted in low-income affordable housing in Mumbai, India. Surveys were conducted in three distinct seasons within a warm–humid climate. The linear regression method yielded a mean neutral temperature of 28.3°C and a wide comfort band ranging from 24.6°C to 32.2°C indicating high thermal adaptation among the occupants. The preferred temperature was found to be 26.3°C. Adaptive comfort standards, ASHRAE and the National Building Code of India prescribed a narrow range of comfort and were ineffective in predicting comfort conditions within affordable housing units. The role of income in shaping comfort expectations was established, lending support to the economic dimension of comfort. The results would be helpful in providing design recommendations for the future affordable housing stock and the development of an adaptive comfort model for vulnerable low-income communities. [ABSTRACT FROM AUTHOR]